Abstract
As the existing DGA detection methods always don’t take into account the problem of word-based DGA method, this will make it invalid. In this paper, a detection method against the word-based DGA has been proposed. Firstly, the word-based DGA methods are analyzed and three type features that the word feature, part-of-speech feature and word correlation feature are analyzed. Then 16 features are concluded from the above analysis and two typical word-based DGA methods Matsnu and Suppobox are chosen as the test object. Finally, the random forest classifier is used in detection. The comparison experimental results show that the proposed method has better performance than the existing ones.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grants nos. 61702235, 61602247, 61472188, and U1636117), Natural Science Foundation of Jiangsu Province (Grants no. BK20160840 and BK20150472), CCF-VENUSTECH Foundation (Grant no. 2016011), and Fundamental Research Funds for the Central Universities (30920140121006 and 30915012208).
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Yang, L. et al. (2018). A Novel Detection Method for Word-Based DGA. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_43
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DOI: https://doi.org/10.1007/978-3-030-00009-7_43
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